Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus

The discovery of giant viruses in unicellular eukaryotic hosts has raised new questions on the nature of viral life. Although many steps in the infection cycle of giant viruses have been identified, the quantitative life history traits associated with giant virus infection remain unknown or poorly c...

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Main Authors: Bradford P. Taylor, Joshua S. Weitz, Corina P. D. Brussaard, Matthias G. Fischer
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Viruses
Subjects:
Online Access:http://www.mdpi.com/1999-4915/10/9/468
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spelling doaj-1dd310218a10459193c46109f4e5d48b2020-11-24T21:47:44ZengMDPI AGViruses1999-49152018-08-0110946810.3390/v10090468v10090468Quantitative Infection Dynamics of Cafeteria Roenbergensis VirusBradford P. Taylor0Joshua S. Weitz1Corina P. D. Brussaard2Matthias G. Fischer3Program for Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USASchool of Biological Sciences and School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USADepartment of Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute of Sea Research, and University of Utrecht, P.O. Box 59, 1790 AB Den Burg, Texel, The NetherlandsDepartment of Biomolecular Mechanisms, Max Planck Institute for Medical Research, 69120 Heidelberg, GermanyThe discovery of giant viruses in unicellular eukaryotic hosts has raised new questions on the nature of viral life. Although many steps in the infection cycle of giant viruses have been identified, the quantitative life history traits associated with giant virus infection remain unknown or poorly constrained. In this study, we provide the first estimates of quantitative infection traits of a giant virus by tracking the infection dynamics of the bacterivorous protist Cafeteria roenbergensis and its lytic virus CroV. Leveraging mathematical models of infection, we quantitatively estimate the adsorption rate, onset of DNA replication, latency time, and burst size from time-series data. Additionally, by modulating the initial ratio of viruses to hosts, we also provide evidence of a potential MOI-dependence on adsorption and burst size. Our work provides a baseline characterization of giant virus infection dynamics relevant to ongoing efforts to understand the ecological role of giant viruses.http://www.mdpi.com/1999-4915/10/9/468giant virusesmultiple infectionsvirus factoriesinfection modelingCroV
collection DOAJ
language English
format Article
sources DOAJ
author Bradford P. Taylor
Joshua S. Weitz
Corina P. D. Brussaard
Matthias G. Fischer
spellingShingle Bradford P. Taylor
Joshua S. Weitz
Corina P. D. Brussaard
Matthias G. Fischer
Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus
Viruses
giant viruses
multiple infections
virus factories
infection modeling
CroV
author_facet Bradford P. Taylor
Joshua S. Weitz
Corina P. D. Brussaard
Matthias G. Fischer
author_sort Bradford P. Taylor
title Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus
title_short Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus
title_full Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus
title_fullStr Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus
title_full_unstemmed Quantitative Infection Dynamics of Cafeteria Roenbergensis Virus
title_sort quantitative infection dynamics of cafeteria roenbergensis virus
publisher MDPI AG
series Viruses
issn 1999-4915
publishDate 2018-08-01
description The discovery of giant viruses in unicellular eukaryotic hosts has raised new questions on the nature of viral life. Although many steps in the infection cycle of giant viruses have been identified, the quantitative life history traits associated with giant virus infection remain unknown or poorly constrained. In this study, we provide the first estimates of quantitative infection traits of a giant virus by tracking the infection dynamics of the bacterivorous protist Cafeteria roenbergensis and its lytic virus CroV. Leveraging mathematical models of infection, we quantitatively estimate the adsorption rate, onset of DNA replication, latency time, and burst size from time-series data. Additionally, by modulating the initial ratio of viruses to hosts, we also provide evidence of a potential MOI-dependence on adsorption and burst size. Our work provides a baseline characterization of giant virus infection dynamics relevant to ongoing efforts to understand the ecological role of giant viruses.
topic giant viruses
multiple infections
virus factories
infection modeling
CroV
url http://www.mdpi.com/1999-4915/10/9/468
work_keys_str_mv AT bradfordptaylor quantitativeinfectiondynamicsofcafeteriaroenbergensisvirus
AT joshuasweitz quantitativeinfectiondynamicsofcafeteriaroenbergensisvirus
AT corinapdbrussaard quantitativeinfectiondynamicsofcafeteriaroenbergensisvirus
AT matthiasgfischer quantitativeinfectiondynamicsofcafeteriaroenbergensisvirus
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